Three Dimensional Pseudo-Spectral Compressible Magnetohydrodynamic GPU Code for Astrophysical Plasma Simulation

This paper presents the benchmarking and scaling studies of a GPU accelerated three dimensional compressible magnetohydrodynamic code. The code is developed keeping an eye to explain the large and intermediate scale magnetic field generation is cosmos as well as in nuclear fusion reactors in the light of the theory given by Eugene Newman Parker. The spatial derivatives of the code are pseudo-spectral method based and the time solvers are explicit. GPU acceleration is achieved with minimal code changes through OpenACC parallelization and use of NVIDIA CUDA Fast Fourier Transform library (cuFFT). NVIDIAs unified memory is leveraged to enable over-subscription of the GPU device memory for seamless out-of-core processing of large grids. Our experimental results indicate that the GPU accelerated code is able to achieve upto two orders of magnitude speedup over a corresponding OpenMP parallel, FFTW library based code, on a NVIDIA Tesla P100 GPU. For large grids that require out-of-core processing on the GPU, we see a 7x speedup over the OpenMP, FFTW based code, on the Tesla P100 GPU. We also present performance analysis of the GPU accelerated code on different GPU architectures - Kepler, Pascal and Volta

Resource interaction in smallholder farms is linked to farm sustainability: evidence from Indian Sundarbans

Efficient resource utilization in small-scale farms is crucial to achieving farm sustainability through endogenous mechanisms. However, the precise mechanisms to integrate farm resources to achieve farm sustainability are not very clear yet. By capturing the interaction among farm resources as a network phenomenon, we aimed to identify the discrete resource interactions (RIs) associated with higher farm sustainability in different farm types of Indian Sundarbans. First, we assessed the sustainability of 140 integrated farms using a synthesized assessment framework. Then, we considered four network motifs, namely linkage (a one-way link between two resources), reciprocal linkage (a two-way link between two resources), triad (three resources having closed interconnectedness), and the presence of a farm resource at the core of a network. Using RI network data of 140 farms and employing a graph theoretic approach we identified discrete network motifs (i.e., resource interaction) associated with highly sustainable farms in different farm types. We found a predominance of rice, vegetables and pond-based integration and identified 32 linkages, 11 reciprocal linkages, 21 triads, and three resources at the network core that occurred and co-occurred on highly sustainable farms, and thus critical to achieving farm sustainability. Further, multivariate analyses established that the properties of RI networks could explain farm sustainability significantly. We anticipate that sustainability in small-scale farms can be achieved by strategically designing new RIs on the farm. However, there may be limitations to such achievement depending on the nature of RI and the type of farm